Series indexing and selection in Data Analysis Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of selecting elements with mixed index types

What is the output of this code snippet?

Data Analysis Python

import pandas as pd
s = pd.Series([10, 20, 30, 40], index=['a', 1, 'b', 2])
result = s.loc[[1, 'b']]
print(result)

A KeyError: '[1, 'b'] not found in axis'

B

a     10
b     30
dtype: int64

C

1    20
2    40
dtype: int64

D

1     20
b     30
dtype: int64

💡 Hint

Remember that .loc uses label-based indexing and can handle mixed types in the index.

The Series has mixed index labels: 'a', 1, 'b', 2. Using .loc with [1, 'b'] selects the elements with labels 1 and 'b', which correspond to values 20 and 30.

❓ data_output

intermediate

Number of elements selected by slicing with .iloc

How many elements are selected by this code?

Data Analysis Python

import pandas as pd
s = pd.Series(range(10), index=list('abcdefghij'))
subset = s.iloc[3:7]
print(len(subset))

A 3

B 4

C 5

D 7

💡 Hint

Remember that slicing with iloc is end-exclusive.

The slice 3:7 selects elements at positions 3,4,5,6, which is 4 elements.

🔧 Debug

advanced

Identify the error in this Series selection code

What error does this code raise?

Data Analysis Python

import pandas as pd
s = pd.Series([1,2,3], index=[0,1,2])
result = s.loc[1:3]
print(result)

A No error, outputs elements with labels 1 and 2

B IndexError: index out of range

C TypeError: unhashable type

D KeyError: 3

💡 Hint

.loc slicing with labels is inclusive of the stop label.

Using .loc with slice 1:3 selects labels 1 and 2 because label 3 does not exist, but slicing stops at the last valid label before 3.

🧠 Conceptual

advanced

Understanding difference between .loc and .iloc with duplicate indices

Given a Series with duplicate indices, which statement is true about .loc and .iloc?

A .loc returns all rows matching the label, .iloc returns rows by position regardless of duplicates

B .loc returns only the first matching label, .iloc returns all positions of that label

C .loc and .iloc behave identically with duplicate indices

D .loc raises an error if duplicates exist, .iloc does not

💡 Hint

Think about label-based vs position-based selection.

.loc selects all rows with the given label, even if duplicated. .iloc selects rows by integer position, ignoring labels.

🚀 Application

expert

Select elements conditionally using Series indexing

Given the Series below, which code snippet correctly selects all elements with values greater than 15 and index labels that are strings?

Data Analysis Python

import pandas as pd
s = pd.Series([10, 20, 30, 40], index=['a', 1, 'b', 2])

A s[(s > 15) & (s.index.map(type) == str)]

B s.loc[(s > 15) & (s.index.map(type) == str)]

C s[(s > 15) & (s.index.map(lambda x: isinstance(x, str)))]

D s.loc[(s > 15) & (s.index.map(lambda x: isinstance(x, str)))]

💡 Hint

Use .map with a function to check index types and combine conditions properly.

Option C correctly uses .map with a lambda to check if index labels are strings and combines it with the value condition. Options A and C compare arrays incorrectly, and D uses .loc with a boolean index which is valid but the condition in D is same as B but .loc is not needed here.

Practice

1. What does series['a'] do in a pandas Series with index labels?

easy

A. Returns a slice from start to label 'a'

B. Selects the value at label 'a' in the Series

C. Selects the value at position 0 in the Series

D. Raises an error because 'a' is not a position

Solution

1. Step 1: Understand label-based indexing

   Using series['a'] accesses the value at the index label 'a', not position.

2. Step 2: Differentiate label vs position

   Label 'a' is a named index, so it returns the value at that label.

3. Final Answer:

   Selects the value at label 'a' in the Series -> Option B

4. Quick Check:

   Label indexing = value at label [OK]

Hint: Use labels inside quotes to get values by label [OK]

Common Mistakes:

• Confusing label with position indexing
• Thinking 'a' is a position index
• Expecting a slice instead of single value

2. Which of these is the correct syntax to select multiple labels 'a' and 'c' from a Series named ser?

easy

A. ser.loc[['a', 'c']]

B. ser['a', 'c']

C. ser.loc['a', 'c']

D. ser.iloc[['a', 'c']]

Solution

1. Step 1: Understand selection of multiple labels

   To select multiple labels, use loc with a list of labels inside double brackets.

2. Step 2: Check syntax correctness

   ser.loc[['a', 'c']] is correct; distractors B and C use tuples (KeyError), D uses iloc which expects positions (TypeError).

3. Final Answer:

   ser.loc[['a', 'c']] -> Option A

4. Quick Check:

   Use loc with list for multiple labels [OK]

Hint: Use ser.loc[[labels]] to select multiple labels [OK]

Common Mistakes:

• Using single brackets for multiple labels
• Omitting loc when selecting multiple labels
• Passing labels as separate arguments instead of a list

3. Given the Series:

import pandas as pd
ser = pd.Series([10, 20, 30, 40], index=['a', 'b', 'c', 'd'])
print(ser['b':'d'])

What is the output?

medium

A. b 20
c 30
d 40
dtype: int64

B. b 20
c 30
dtype: int64

C. 20
30
40

D. 20
30

Solution

1. Step 1: Understand label slicing in Series

   Label slicing with ser['b':'d'] includes both start and end labels.

2. Step 2: Identify values for labels 'b' to 'd'

   Labels 'b', 'c', and 'd' correspond to values 20, 30, and 40 respectively.

3. Final Answer:

   b 20 c 30 d 40 dtype: int64 -> Option A

4. Quick Check:

   Label slice includes end label [OK]

Hint: Label slices include the end label [OK]

Common Mistakes:

• Excluding the end label in slice
• Confusing label slicing with position slicing
• Expecting only values without index labels

4. What is wrong with this code snippet?

import pandas as pd
ser = pd.Series([1, 2, 3], index=['x', 'y', 'z'])
print(ser[1:2])

Why might this cause confusion?

medium

A. It raises a KeyError because 1 and 3 are not labels

B. It returns a single value instead of a slice

C. It returns values for labels 'y' and 'z' inclusive

D. It slices by position, excluding index label 'z'

Solution

1. Step 1: Understand position slicing in Series

   Using ser[1:2] slices by position, not label, and excludes the end position.

2. Step 2: Identify slice range and output

   Position 1 (inclusive) to 2 (exclusive) corresponds to label 'y' (position 1), excluding position 2 ('z').

3. Final Answer:

   It slices by position, excluding index label 'z' -> Option D

4. Quick Check:

   Position slices exclude end position [OK]

Hint: Position slices exclude the end index [OK]

Common Mistakes:

• Expecting label slicing with integer slice
• Thinking slice includes end position
• Confusing KeyError with slicing behavior

5. You have a Series ser with index labels ['a', 'b', 'c', 'd', 'e'] and values [5, 10, 15, 20, 25]. How do you select all values greater than 10 using Series indexing and selection?

hard

A. ser[ser.index > 10]

B. ser.index[ser > 10]

C. ser[ser > 10]

D. ser.loc[ser.index > 10]

Solution

1. Step 1: Use boolean indexing on Series values

   Comparing ser > 10 creates a boolean mask for values greater than 10.

2. Step 2: Apply boolean mask to Series

   Using ser[ser > 10] selects values where the mask is True.

3. Final Answer:

   ser[ser > 10] -> Option C

4. Quick Check:

   Boolean mask on values selects correct subset [OK]

Hint: Use ser[ser > value] to filter by value condition [OK]

Common Mistakes:

• Trying to filter by index values instead of data values
• Using loc with boolean mask on index instead of values
• Confusing index labels with values in condition